As the world becomes increasingly digitized, chatbots are becoming increasingly popular. They are an efficient way to communicate with customers, provide instant support, and automate tasks. Chatbots can be programmed to accomplish specific tasks, answer questions, and even converse with users. This blog post will concern how to build and deploy your ChatGPT AI website using JavaScript.

Introduction to Chatbots

Before we dive into the technical details of building a ChatGPT AI website, let’s take a moment to understand what chatbots are and how they work.

What are chatbots?

Chatbots are computer programs designed to emulate conversations with human users. They can be programmed to perform various tasks, from answering simple questions to providing customer support.

How do chatbots work?

Chatbots use natural language processing (NLP) and machine learning (ML) algorithms to comprehend and react to user inputs. They can be programmed to recognize specific keywords and phrases and respond accordingly.

Introduction to ChatGPT

ChatGPT is an open-source AI model developed by OpenAI. It is based on the GPT-2 architecture and is prepared on a large corpus of text data. As a result, ChatGPT can generate human-like responses to text inputs and be used to build chatbots.

What is ChatGPT?

ChatGPT is an AI model developed by OpenAI that can generate human-like replies to text inputs. It is based on the GPT-2 architecture and is prepared on a large corpus of text data.

How does ChatGPT work?

ChatGPT uses a deep neural network to generate responses to text inputs. It is trained on a large corpus of text data and can create replies that are contextually relevant and grammatically correct.

Building a ChatGPT AI Website in JavaScript

Now that we have a fundamental learning of chatbots and ChatGPT, let’s dive into the technical details of building a ChatGPT AI website in JavaScript.

Building a ChatGPT AI website can seem daunting, but with the right tools and coding know-how, you can create a functional chatbot quickly. This section will cover the technical details of building a ChatGPT AI website in JavaScript.

Setting up the Environment

Before building our ChatGPT AI website, we need to set up our development environment. Here’s what we’ll need:

  1. Node.js: Node.js is a JavaScript runtime environment that allows you to run JavaScript on the server side. We will use Node.js to create our backend.
  2. React.js: A JavaScript library that allows you to build user interfaces. We will use React.js to create our front end.
  3. Next.js: Next.js is a framework for building server-side rendered React applications. We will use Next.js to create our front end.

To install these packages, you must have Node.js installed on your computer. Once you have Node.js installed, you can now open up a terminal in a new window and run the following commands:

				
					# python file

# Install React.js and Next.js
npm install react next

# Install dependencies for the backend
npm install express Axios dotenv
				
			

Creating the Backend

The backend of our ChatGPT AI website will be responsible for processing user inputs and generating responses using ChatGPT. To create the backend, we will use Node.js and the Express.js framework. Here’s a breakdown of the steps involved:

1. Please create a new folder for your project and navigate to it in your terminal.

2. Initialize a new Node.js project by running ‘npm init’.

3. Install the required dependencies by running ‘npm install express axios dotenv’.

4. Create a new file called ‘server.js’.

5. In ‘server.js’, import the necessary dependencies:

				
					# js file

const express = require('express');
const axios = require('axios');
const dotenv = require('dotenv');

dotenv.config();
const app = express();
const port = process.env.PORT || 5000;
				
			

6. Create a route for handling user input:

				
					# js file

app.get('/api/chat', async (req, res) => {
  const { message } = req.query;
  const response = await axios.post('https://api.openai.com/v1/engines/davinci-codex/completions', {
    prompt: message,
    max_tokens: 60,
    n: 1,
    stop: ['\n']
  }, {
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`
    }
  });
  res.send(response.data.choices[0].text);
});
				
			

7. Start the server:

				
					# js file

app.listen(port, () => {
  console.log(`Server running on port ${port}`);
});
				
			

Congratulations, you have just created a basic backend for your ChatGPT AI website!

Creating the Frontend

In this section, we’ll create the front end of our ChatGPT AI website. The front end will display the chat interface and send user inputs to the backend.

We will use HTML, CSS, and JavaScript to create the front end. We’ll start by creating an HTML file called “index.html” and adding the necessary markup for the chat interface. Here’s an essential structure for our HTML file:

				
					# html file

<!DOCTYPE html>
<html>
<head>
	<title>ChatGPT AI</title>
	<link rel="stylesheet" href="style.css">
</head>
<body>
	<div class="chat-container">
		<div class="chat-header">
			<h1>ChatGPT AI</h1>
		</div>
		<div class="chat-messages">
			<ul id="message-list"></ul>
		</div>
		<div class="chat-input">
			<form id="message-form">
				<input type="text" id="message-input" autocomplete="off">
				<button type="submit">Send</button>
			</form>
		</div>
	</div>

	<script src="script.js"></script>
</body>
</html>
				
			

Here, we’ve created a basic HTML structure that includes a container for the chat interface, a header for the chatbot name, a messages area where the conversation will be displayed, and an input area for users to type their messages. We’ve also included links to our CSS and JavaScript files.

Next, we’ll create a CSS file called “style.css” and add the necessary styles to make our chat interface look good. Here’s a primary stylesheet to get us started:

				
					# CSS file

.chat-container {
	width: 400px;
	height: 500px;
	margin: 50px auto;
	background-color: #f1f1f1;
	box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.1);
	border-radius: 10px;
	overflow: hidden;
}

.chat-header {
	background-color: #333;
	color: #fff;
	padding: 10px;
	text-align: center;
}

.chat-messages {
	height: 400px;
	overflow-y: scroll;
}

.chat-messages li {
	padding: 10px;
	list-style: none;
}

.chat-messages li:nth-child(odd) {
	background-color: #e1e1e1;
}

.chat-input {
	padding: 10px;
	display: flex;
	align-items: center;
}

.chat-input input[type="text"] {
	flex: 1;
	padding: 10px;
	border: none;
	border-radius: 5px;
}

.chat-input button {
	background-color: #333;
	color: #fff;
	padding: 10px;
	border: none;
	border-radius: 5px;
	margin-left: 10px;
	cursor: pointer;
}

				
			

Here, we’ve added styles to our HTML elements to create a clean and modern chat interface. We’ve set the width and height of our chat container, added a background color and box shadow, and set the border radius to create rounded corners. We’ve also added styles to our header, messages, and input area to create a consistent look and feel.

Now that we have our HTML and CSS files in place, we’ll create a JavaScript file called “script.js” and write the code to send user inputs to the backend and display the responses in the messages area.

Integrating ChatGPT

To integrate ChatGPT into our website, we will use the OpenAI API. The OpenAI API is a robust tool that allows developers to easily incorporate state-of-the-art AI models like GPT-3 into their applications. Before using the API, we’ll need to create an account and obtain an API key from the OpenAI website.

Once we have our API key, we can request the OpenAI API using the Axios library. Axios is a popular JavaScript library for making HTTP requests, and it is particularly well-suited for working with APIs.

Let’s start by installing Axios:

				
					npm install axios 
				
			

Now let’s create a new file called ‘openai.js’ in the ‘utils’ directory. This file will contain a function that sends a request to the OpenAI API and returns the response:

				
					# javascript file

import axios from 'axios';

const openai = async (input) => {
  const response = await axios.post(
    'https://api.openai.com/v1/engines/davinci-codex/completions',
    {
      prompt: input,
      max_tokens: 150,
      n: 1,
      stop: '\n',
    },
    {
      headers: {
        Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
        'Content-Type': 'application/json',
      },
    }
  );
  return response.data.choices[0].text.trim();
};
export default openai;
				
			

This function takes an input string as an argument and sends a request to the OpenAI API using the ‘axios.post()’ method. The ‘prompt’ field contains the input string, and the ‘max_tokens’ field controls the maximum number of tokens (words or punctuation) in the generated response. The ‘n’ field controls the number of responses to generate, and the stop field specifies a string that the model should ‘stop’ generating at (in this case, a newline character).

The function also includes an ‘Authorization’ header that contains our API key. We’ve stored the API key in an environment variable called ‘OPENAI_API_KEY’, which we can set in a ‘.env’ file in the root directory of our project.

Now that we have our ‘openai()’ function, we can use it to generate responses to user inputs in our chat interface. Let’s modify the ‘handleSubmit()’ function in Chat.js to use the ‘openai()’ function:

				
					# javascript file

const handleSubmit = async (e) => {
  e.preventDefault();
  const text = e.target[0].value;
  appendMessage(text, 'user');
  e.target[0].value = '';
  const response = await openai(text);
  appendMessage(response, 'bot');
};
				
			

Here, we’ve replaced the placeholder response with the result of the ‘openai()’ function. When the user submits a message, our website will send the message to the OpenAI API and display the generated response in the chat interface.

With the integration of ChatGPT, our website can now generate intelligent responses to user inputs. In the next section, we’ll deploy our website to the web using Vercel.

Deploying Your ChatGPT AI Website

After we have built our ChatGPT AI website, we will need to deploy it so that users can access it online. There are a few different options for deploying a Node.js website, and we will discuss some of the most popular choices.

It is a cloud platform that allows you to deploy and manage your applications. It supports Node.js and is a popular option for deploying Node.js websites. To deploy our ChatGPT AI website to Heroku, we must create an account, install the Heroku CLI, and follow simple steps.

AWS Elastic Beanstalk is a cloud platform allowing you to deploy and scale web applications quickly. It supports Node.js and is a popular option for deploying Node.js websites. To deploy our ChatGPT AI website to AWS Elastic Beanstalk, we must create an AWS account, install the AWS CLI, and follow a few simple steps.

DigitalOcean is a cloud platform that allows you to deploy and manage your applications. It supports Node.js and is a popular option for deploying Node.js websites. To deploy our ChatGPT AI website to DigitalOcean, we must create a DigitalOcean account, create a droplet, and follow a few simple steps.

Final Lines

This blog post discussed how to build and deploy your own ChatGPT AI website using JavaScript. First, we covered the basics of chatbots and ChatGPT and walked through the technical details of building a ChatGPT AI website. We also discussed popular options for deploying Node.js, React.js, and Next.js websites, including Heroku, AWS Elastic Beanstalk, and DigitalOcean. With the skills you have learned in this blog post, you can now create your chatbot and deploy it online.